Skip to main content
SpringerLink
Log in

Using DeepLab v3 + -based semantic segmentation to evaluate platelet activation

  • Original Article
  • Published:
Medical & Biological Engineering & Computing Aims and scope Submit manuscript

Abstract

This research used DeepLab v3 + -based semantic segmentation to automatically evaluate the platelet activation process and count the number of platelets from scanning electron microscopy (SEM) images. Current activated platelet recognition and counting methods include (a) using optical microscopy or SEM images to identify and manually count platelets at different stages, or (b) using flow cytometry to automatically recognize and count platelets. However, the former is time- and labor-consuming, while the latter cannot be employed due to the complicated morphology of platelet transformation during activation. Additionally, because of how complicated the transformation of platelets is, current blood-cell image analysis methods, such as logistic regression or convolution neural networks, cannot precisely recognize transformed platelets. Therefore, this study used DeepLab v3 + , a powerful learning model for semantic segmentation of image analysis, to automatically recognize and count platelets at different activation stages from SEM images. Deformable convolution, a pretrained model, and deep supervision were added to obtain additional platelet transformation features and higher accuracy. The number of activated platelets was predicted by dividing the segmentation predicted platelet area by the average platelet area. The results showed that the model counted the activated platelets at different stages from the SEM images, achieving an error rate within 20%. The error rate was approximately 10% for stages 2 and 4. The proposed approach can thus save labor and time for evaluating platelet activation and facilitate related research.

Graphical abstract

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Modic M, Junkar I, Stana-Kleinschek K, Kostanjšek R, Mozetič M, Polymers PJPPa (2014) Morphology transformations of platelets on plasma activated surfaces. Plasma Processes Polym 11(6):596–605. https://doi.org/10.1002/ppap.201400001

    Article  CAS  Google Scholar 

  2. Yun S-H, Sim E-H, Goh R-Y, Park J-I, Han J-Y. 2016 Platelet activation: the mechanisms and potential biomarkers. BioMed research international 2016. https://doi.org/10.1155/2016/9060143.

  3. Cheng K-Y, Lin Z-H, Cheng Y-P, Chiu H-Y, Yeh N-L, Wu T-K, Wu J-S (2018) Wound healing in streptozotocin-induced diabetic rats using atmospheric-pressure argon plasma jet. Sci Rep 8(1):1–15. https://doi.org/10.1038/s41598-018-30597-1

    Article  CAS  Google Scholar 

  4. Miao H, Xiao C (2018) Simultaneous segmentation of leukocyte and erythrocyte in microscopic images using a marker-controlled watershed algorithm. Comput Math Methods Med 2018:1–9. https://doi.org/10.1155/2018/7235795

    Article  Google Scholar 

  5. Roy K, Dey R, Bhattacharjee D, Nasipuri M, Ghosh P. 2016 An automated system for platelet segmentation using histogram-based thresholding. 2016 2nd International Conference on Advances in Computing, Communication, & Automation (ICACCA)(Fall) 1–7. https://doi.org/10.1109/ICACCAF.2016.7749000.

  6. Jiang Y, Lei C, Yasumoto A, Kobayashi H, Aisaka Y, Ito T, Guo B, Nitta N, Kutsuna N, Ozeki Y (2017) Label-free detection of aggregated platelets in blood by machine-learning-aided optofluidic time-stretch microscopy. Lab Chip 17(14):2426–2434. https://doi.org/10.1039/C7LC00396J

    Article  CAS  PubMed  Google Scholar 

  7. Pokrovskaya ID, Yadav S, Rao A, McBride E, Kamykowski JA, Zhang G, Storrie B (2019) 3D ultrastructural analysis of α-granule, dense granule, mitochondria, and canalicular system arrangement in resting human platelets. Res Practice Thromb Haemost 4(1):72–85. https://doi.org/10.1002/rth2.12260

    Article  CAS  Google Scholar 

  8. Kihm A, Kaestner L, Wagner C, Quint S (2018) Classification of red blood cell shapes in flow using outlier tolerant machine learning. PLoS Comput Biol 14(6):e1006278. https://doi.org/10.1371/journal.pcbi.1006278

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Xu M, Papageorgiou DP, Abidi SZ, Dao M, Zhao H, Karniadakis GE (2017) A deep convolutional neural network for classification of red blood cells in sickle cell anemia. PLoS Comput Biol 13(10):e1005746. https://doi.org/10.1371/journal.pcbi.1005746

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Zhang M, Li X, Xu M, Li Q. 2018 RBC semantic segmentation for sickle cell disease based on deformable U-Net. International Conference on Medical Image Computing and Computer-Assisted Intervention 695–702. https://doi.org/10.1007/978-3-030-00937-3_79.

  11. Shahzad M, Umar A.I, Khan M.A, Shirazi S.H, Khan Z, Yousaf W. 2020 Robust method for semantic segmentation of whole-slide blood cell microscopoc images. Computational and mathematical method in medicine 1–13. https://doi.org/10.1155/2020/4015323.

  12. Guay M-D, Emam Z-A, Anderson A-B, Aronova M-A, Pokrovskaya I-D, Storrie B, Leapman R-D (2021) Dense cellular segmentation for EM using 2D–3D neutral network ensemble. Sci Rep 11(1):1–11. https://doi.org/10.1038/s41598-021-81590-0

    Article  CAS  Google Scholar 

  13. L-C Chen Y Zhu G Papandreou F Schroff H Adam 2018 Encoder-decoder with atrous separable convolution for semantic image segmentation Proc Eur Confer Comput Vis (ECCV) 801–818. https://doi.org/10.1007/978-3-030-01234-2_49

  14. Lee C-Y, Xie S, Gallagher P, Zhang Z, Tu Z. 2015 Deeply-supervised nets. Artificial intelligence and statistics 562–570.

  15. Dai J, Qi H, Xiong Y, Li Y, Zhang G, Hu H, Wei Y. 2017 Deformable convolutional networks. Proceedings of the IEEE international conference on computer vision 764–773. https://doi.org/10.1109/ICCV.2017.89.

  16. He K, Zhang X, Ren S, Sun J. 2016 Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition 770–778. https://doi.org/10.1109/CVPR.2016.90.

  17. Deng J, Dong W, Socher R, Li L-J, Li K, Fei-Fei L (2009) Imagenet: a large-scale hierarchical image database. IEEE Confer Comput Vis Pattern Recognit 2009:248–255. https://doi.org/10.1109/CVPR.2009.5206848

    Article  Google Scholar 

  18. Simonyan KZisserman A. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 2014. arXiv:1409.1556

  19. Poursafa P, Kelishadi R (2010) Air pollution, platelet activation and atherosclerosis. Inflamm Allergy-Drug Targets (Formerly Current Drug Targets-Inflammation & Allergy) 9(5):387–392. https://doi.org/10.2174/187152810793937982

    Article  CAS  Google Scholar 

Download references

Acknowledgements

The authors would like to thank Prof. Chong-Sin Wu for providing the experiment equipment.

Author information

Authors and Affiliations

  1. Department of Mechanical Engineering, College of Engineering, National Chiao Tung University, Hsin-Chu, Taiwan

    Tsung-Chen Kuo, Ting-Wei Cheng, Ching-Kai Lin, Ming-Che Chang, Kuang-Yao Cheng & Yun-Chien Cheng

  2. Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan

    Ching-Kai Lin

  3. Department of Internal Medicine, National Taiwan University Hospital Hsin-Chu Branch, Hsin-Chu, Taiwan

    Ching-Kai Lin

Authors
  1. Tsung-Chen Kuo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ting-Wei Cheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ching-Kai Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ming-Che Chang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Kuang-Yao Cheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Yun-Chien Cheng
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yun-Chien Cheng.

Ethics declarations

Conflict of interest

The authors declare no competing interests.

Additional information

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kuo, TC., Cheng, TW., Lin, CK. et al. Using DeepLab v3 + -based semantic segmentation to evaluate platelet activation. Med Biol Eng Comput 60, 1775–1785 (2022). https://doi.org/10.1007/s11517-022-02575-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11517-022-02575-3

Keywords

Search

Navigation